A customer sees an ad for your sale on a third-party website. It is tempting but they decide to park that thought for now. In a couple of days, they give in to the temptation and look up your website on a search engine. And when they land on your website, they see a banner of the exact same ad! A feeling of deja vu…and you have a purchase in less than five minutes. And this customer is now a regular! If you are a marketer reading this, you are curious about where this perfect customer is. If you are the founder of the company, you are wondering why your marketer can’t find them! All of us are looking for this perfect customer, aren’t we?

What most of us fail to realize is that with advancement in big data today, we needn’t wait for this ‘perfect’ customer to arrive. We can actually induce the behavior we want and create that perfect customer. That is where most of us are failing. We may have all the data we want and need, we have all the channels to target the ‘right’ customer but most of us are not reading between the lines. Sounds familiar?

Let’s look at what companies and marketers are not doing right.

Campaigns are created independent of data.

In my experience, there are very few companies that are collecting data and using it right. Simple as it seems, understanding attributes and defining data points that need to be collected is a complex affair. Languages like R, SAS and Python can help achieve this kind of a complex analysis.

Next comes using data right. Every user action can be captured but what use is it if this data is not put to use. Rather than use data as a validation tool to measure efficacy of campaigns, data must be the starting point of creating one.

Problems are analyzed in silos rather than being connected with journeys.

“Sometimes the questions are complicated but the answers are simple.” I cannot stress enough on this powerful quote by Dr Seuss.

By looking at problems independent of user journeys on your platform, you won’t get very far or succeed. Using customer navigation on your platform and mapping those to problems that you are trying to solve, is doing two things – improving your website’s user experience and solving for a marketing problem.

For e.g. you notice that drop off usually happens between 90-95 seconds. By creating a pop-up that shows customized content, based on where the customer landed first during that same period, could help prevent that drop-off.

Companies are trying to solve many problems at the same time.

But that is how it should be, right? Wrong. By trying to solve a lot of problems at the same time, a campaign turns out to be much more generalized instead of being customized to address a specific problem. Focusing on a single problem at a time will help understand data points that need to be monitored.

For instance, we identified a particular problem for one of our customers – reducing CAC. We connected the dots and came to the conclusion that users were looking for very specific information and that differed based on location. Accordingly, the solution proposed was personalizing content based on user location and search query. We were able to bring up the conversion rate from 5.6% to 17.2%, an increase by almost three times, by showing customized content.

Only 16% of the companies globally are confident about their analytics solution and the main reason for this is lack of data analyst who can bring meaning to the data and ask the right questions to companies.

Data tools are used diagnostically rather than in a predictive manner.

Despite advancement in data, companies are still using data to retrospectively understand why customers behaved the way they did. In today’s fast-paced world with diminishing attention spans and thousands of elements vying for the user’s attention, data needs to be used predictively. Rather than catching up with the customer, we need to be able to analyze what the customer is likely to do next, beat them to it and make that experience awesome! Leveraging regressions and statistical models can help predict interests and sale in the future. Platforms like Monkeylearn, Tableau, CanisHub, Watson, among others can help do just that. I prefer to use startups like CanisHub as they are more action driven and use predictive model to improve customer experience of e-commerce sites; all the more important as survey says, 91% of the customers don’t want to do business with sites with poor experience and with portals like Amazon, Ebay, Flipkart in competition, it is important to scale your game.

So getting a customer to buy a product is great but we need to go beyond. If a user buys product X, what is the probability of that user buying another product? What product would that be? How long will it take the user to buy that product? What is the spending expectation for that user? And so on… Going by your gut feeling is good but going by data is even better!

Technologies that were earlier limited to the scientific community are now available to us, marketers. With advanced data mining and analytics, even small businesses and enterprises can use data to solve marketing problems. From seemingly simple questions like, when is the customer likely to make the second purchase to complex ones like, segmenting consumers and what attributes to consider, data can help get many such answers. All we need to do is decode the data.